Methods, systems, articles of manufacture, and apparatus to recalibrate confidences for image classification

ABSTRACT

Methods, systems, articles of manufacture, and apparatus to recalibrate confidences for image classification are disclosed. An example apparatus to classify an image includes an image crop detector to detect a first image crop from the image, the first image crop corresponding to a first object, a grouping controller to select a second image crop corresponding to a second object at a location of the first object, a prediction generator to, in response to executing a trained model, determine a label corresponding to the first object and a confidence level associated with the label, and a confidence recalibrator to recalibrate the confidence level based on a probability of the first object having a first attribute based on the second object having a second attribute, the confidence level recalibrated to increase an accuracy of the image classification.

RELATED APPLICATION

This patent arises from a continuation of U.S. application Ser. No.17/102,064 (now U.S. Pat. No. 11,544,508), titled “Methods, Systems,Articles of Manufacture, and Apparatus to Recalibrate Confidences forImage Classification,” filed Nov. 23, 2020, which is hereby incorporatedby this reference in its entirety.

FIELD OF THE DISCLOSURE

This disclosure relates generally to image classification and, moreparticularly, to methods, systems, articles of manufacture, andapparatus to recalibrate confidences for image classification.

BACKGROUND

In retail environments, information pertaining to product inventory in astore, product types, and availability can be used to gain a greaterunderstanding of consumer demand and trends. Such information can begathered from image data. In particular, images taken of product shelvesin a retail setting can be analyzed using various techniques to analyzeretail, market and/or consumer data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates example image crops of products from a retailenvironment that can be analyzed in examples disclosed herein.

FIG. 2 illustrates an example detection and classification methodologyfor which examples disclosed herein can be implemented.

FIG. 3 illustrates an example confidence generation and recalibrationprocess flow in accordance with teachings of this disclosure.

FIG. 4 is a block diagram of an example classification controller inaccordance with teachings of this disclosure.

FIG. 5A illustrates an example volume co-occurrence cardinality graphthat can be implemented in examples disclosed herein.

FIG. 5B illustrates an example volume co-occurrence distribution graphthat can be implemented in examples disclosed herein.

FIG. 6 is a block diagram of an example process flow that can beimplemented in examples disclosed herein.

FIG. 7 is a block diagram of an example graph generation process flowthat can be implemented in examples disclosed herein.

FIG. 8 is a block diagram of an example recalibration process flowexecuted to recalibrate confidence levels that can be implemented inexamples disclosed herein.

FIGS. 9-11 are flowcharts representative of machine readableinstructions which may be executed to implement the exampleclassification controller of FIG. 4 .

FIG. 12 is a block diagram of an example processing platform structuredto execute the instructions of FIGS. 9, 10 , and/or 11 to implement theexample classification controller of FIG. 4 .

The figures are not to scale. Instead, the thickness of the layers orregions may be enlarged in the drawings. In general, the same referencenumbers will be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

Descriptors “first,” “second,” “third,” etc. are used herein whenidentifying multiple elements or components which may be referred toseparately. Unless otherwise specified or understood based on theircontext of use, such descriptors are not intended to impute any meaningof priority, physical order or arrangement in a list, or ordering intime but are merely used as labels for referring to multiple elements orcomponents separately for ease of understanding the disclosed examples.In some examples, the descriptor “first” may be used to refer to anelement in the detailed description, while the same element may bereferred to in a claim with a different descriptor such as “second” or“third.” In such instances, it should be understood that suchdescriptors are used merely for ease of referencing multiple elements orcomponents.

DETAILED DESCRIPTION

In retail environments, information pertaining to an inventory ofproducts in a store can be gathered and utilized to identify informationabout marketing, product display and/or consumer trends. The informationcan include a number of the products in the store, types of theproducts, availability of the products, etc. In some cases, an auditorcan visit the store and capture images of store shelves in the store(e.g., using a camera and/or a mobile device). Subsequently, the imagescan be provided to a computer/computational system for processing, wherethe processing can entail detecting and classifying products from theimages using an artificial intelligence (AI) model (e.g., a machinelearning model, a neural network (CNN) model, etc.). In some cases, theAI model can be a convolutional neural network (CNN) model.

Artificial intelligence (AI), including machine learning (ML), deeplearning (DL), and/or other artificial machine-driven logic, enablesmachines (e.g., computers, logic circuits, etc.) to use a model toprocess input data to generate an output based on patterns and/orassociations previously learned by the model via a training process. Forinstance, the model may be trained with data to recognize patternsand/or associations and follow such patterns and/or associations whenprocessing input data such that other input(s) result in output(s)consistent with the recognized patterns and/or associations.

Many different types of machine learning models and/or machine learningarchitectures exist. In examples disclosed herein, a convolutionalneural network (CNN) model is used. Using a CNN model enables detectionand classification of products from images without a need for humanintervention. In general, machine learning models/architectures that aresuitable to use in the example approaches disclosed herein will be blackbox networks in which interconnections are not visible outside of themodel. However, other types of machine learning models couldadditionally or alternatively be used.

In general, implementing a ML/AI system involves two phases, alearning/training phase and an inference phase. In the learning/trainingphase, a training algorithm is used to train a model to operate inaccordance with patterns and/or associations based on, for example,training data. In general, the model includes internal parameters thatguide how input data is transformed into output data, such as through aseries of nodes and connections within the model to transform input datainto output data. Additionally, hyperparameters are used as part of thetraining process to control how the learning is performed (e.g., alearning rate, a number of layers to be used in the machine learningmodel, etc.). Hyperparameters are defined to be training parameters thatare determined prior to initiating the training process.

Different types of training may be performed based on the type of ML/AImodel and/or the expected output. For example, supervised training usesinputs and corresponding expected (e.g., labeled) outputs to selectparameters (e.g., by iterating over combinations of select parameters)for the ML/AI model that reduce model error. As used herein, labellingrefers to an expected output of the machine learning model (e.g., aclassification, an expected output value, etc.) Alternatively,unsupervised training (e.g., used in deep learning, a subset of machinelearning, etc.) involves inferring patterns from inputs to selectparameters for the ML/AI model (e.g., without the benefit of expected(e.g., labeled) outputs).

In examples disclosed herein, ML/AI models are trained using gradientdescent. However, any other training algorithm may additionally oralternatively be used. In examples disclosed herein, training isperformed until an acceptable amount of error associated with a crossentropy loss function is achieved. In examples disclosed herein,training is performed remotely at a computer system. Training isperformed using hyperparameters that control how the learning isperformed (e.g., a learning rate, a number of layers to be used in themachine learning model, etc.).

Training is performed using training data. In examples disclosed herein,the training data originates from images that have been reviewedmanually by a human. Because supervised training is used the trainingdata is labeled. Labeling is applied to the training data by the humanduring manual review of the image data. In some examples, the trainingdata is pre-processed using, for example, an image processor to resizeand/or reshape the images. In some examples, the training data issub-divided into training data and validation data.

Once training is complete, the model is deployed for use as anexecutable construct that processes an input and provides an outputbased on the network of nodes and connections defined in the model. Themodel is stored in a database implemented by any memory, storage deviceand/or storage disc for storing data such as, for example, flash memory,magnetic media, optical media, solid state memory, hard drive(s), thumbdrive(s), etc. The model may then be executed by the exampleclassification controller described in detail below in connection withFIG. 4 .

Once trained, the deployed model may be operated in an inference phaseto process data. In the inference phase, data to be analyzed (e.g., livedata) is input to the model, and the model executes to create an output.This inference phase can be thought of as the AI “thinking” to generatethe output based on what it learned from the training (e.g., byexecuting the model to apply the learned patterns and/or associations tothe live data). In some examples, input data undergoes pre-processingbefore being used as an input to the machine learning model. Moreover,in some examples, the output data may undergo post-processing after itis generated by the AI model to transform the output into a usefulresult (e.g., a display of data, an instruction to be executed by amachine, etc.).

In some examples, output of the deployed model may be captured andprovided as feedback. By analyzing the feedback, an accuracy of thedeployed model can be determined. If the feedback indicates that theaccuracy of the deployed model is less than a threshold or othercriterion, training of an updated model can be triggered using thefeedback and an updated training data set, hyperparameters, etc., togenerate an updated, deployed model.

According to examples disclosed herein, a first CNN model can be trainedand executed to detect image crops (e.g., cropped images) correspondingto the products in images obtained in a retail environment.Additionally, a second CNN model can be executed based on the imagecrops and predictions can be generated corresponding to each of theimage crops. Such predictions can include a unique product descriptionand/or attribute, such as a volume of the product corresponding to eachof the image crops, for example. Furthermore, the computer system cangenerate confidence levels associated with each of the predictions,where the confidence levels indicate a likelihood or probability thatthe corresponding predictions are correct and/or accurate. In someexamples, multiple predictions are generated for each of the imagecrops, where the multiple predictions have varying confidence levels. Insuch cases, to classify and/or otherwise identify the product in each ofthe image crops, the computer system can be configured to select theprediction corresponding to the highest confidence level. As such, thecomputer system can classify and/or otherwise identify each of theproducts on the store shelves captured in the images.

Advantageously, applying CNN models to classify products from imagesrequires little to no human intervention. As such, automatedclassification of the images based on the CNN models can generally beexecuted in less time compared to manual classification in which theimages are reviewed by a human reviewer. However, in some cases,challenges associated with the automated classification are related to alarge number of unique products to be identified (e.g., over 50,000 insome countries) and/or a potential presence of noise in the trainingdata for the CNN models. Furthermore, the CNN models may not be able toaccurately distinguish between products having a similar appearance. Forexample, two products may be similar in appearance, but differ in anattribute, such as a volume (e.g., a 2.2 liter cola and a 2 liter cola).Typically, such products may be distinguished by a human based on sizeof the products and/or text of the products indicating the volumes.These attributes may be difficult to read on a camera or image sensor.In particular, size/volume information may not be captured and/orascertained during the process of obtaining the images. In particular,the images may be captured at irregular distances, such that sizes ofthe products in the images may vary (e.g., due to varying distances fromthe products at which the images were captured, due to image and/or lensdistortions of product sizes, etc.). Furthermore, the text of theproducts may not be visible and/or detectable for data accessed by theCNN models due to the products being oriented at irregular angles and/orin response to the images being blurry, for example. As such, the CNNmodels may not be able to ascertain size or volume information and/ortext to distinguish attributes between the products for which images arecaptured.

Examples disclosed herein recalibrate the confidence levels predicted bythe CNN models by integrating contextual information into the predictedconfidence levels. For example, examples disclosed herein generate aprobability distribution based on co-occurrence of characteristics(e.g., volumes) of products in a shelf. Examples disclosed hereinutilize the probability distribution to update the predicted confidencelevels based on the probability of each pair of the products beinglocated on the same shelf and/or at a proximate relative positioningthereof. Advantageously, examples disclosed herein improve accuracy ofproduct classifications predicted by the CNN models.

FIG. 1 illustrates example image crops 102, 104, 106, 108 of productsfrom a retail environment that can be analyzed in examples disclosed. Inthis example, the products have different corresponding volumes. In theillustrated example of FIG. 1 , the example first image crop 102corresponds to a first product (e.g., a first Sprite beverage producthaving a volume of 1500 cubic centimeters (cc)), the example secondimage crop 104 corresponds to a second product (e.g., a second Sprite®beverage product having a volume of 2250 cc), the example third imagecrop 106 corresponds to a third product (e.g., a first CocaCola®beverage product having a volume of 2250 cc), and the example fourthimage crop 108 corresponds to a fourth product (e.g., a secondCoca-Cola® beverage product having a volume of 3000 cc). In someexamples, the image crops 102, 104, 106, 108 are detected from an imageutilized by a CNN model. In some examples, the products corresponding tothe image crops 102, 104, 106, 108 are located on one or more shelves ina store.

In the illustrated example of FIG. 1 , the first product of the firstimage crop 102 and the second product of the second image crop 104correspond to a first product type 110 (e.g., Sprite beverage), and thethird product of the third image crop 106 and the fourth product of thefourth image crop 108 correspond to a second product type 112 (e.g.,Coca-Cola® beverage). Accordingly, the first product and the secondproduct have similar appearances (e.g., similar shape, color, labeldesign, etc.), but different volumes. Similarly, to differentiatedifferent products of the first and second product types 110, 112, thevolumes of the products can be predicted using the CNN model.

The volume information can be predicted based on size of the productsand/or text indicating attributes of the products. However, sizeinformation can be lost and/or distorted during capturing of the imagescorresponding to the image crops 102, 104, 106, 108. For example, thesecond product in the second image crop 104 has a larger volume comparedto the first product in the first image crop 102 and, as such, thesecond product is typically larger in size compared to the firstproduct. However, based on capturing the first image crop 102 and thesecond image crop 104 at different distances and/or angles from thecorresponding products, the first product in the first image crop 102can inaccurately appear to be the same or similar size as the secondproduct in the second image crop 104. As such, the size of the imagecrops 102, 104, 106, 108 may not provide accurate information about thesizes and/or volumes of the corresponding products. Additionally, thetext on product labels indicating the volumes of the correspondingproducts may not be visible, legible and/or detectable by the CNN model.For example, the text may be less visible or discernible when rotatedaway from a camera capturing the image crops 102, 104, 106, 108. Assuch, the CNN model may be unable to accurately classify the productsbased on individual ones of the image crops 102, 104, 106, 108.

FIG. 2 illustrates an example detection and classification methodology200 for which examples disclosed herein can be implemented. Thedetection and classification methodology 200 includes an exampledetection phase 202 and an example classification phase 204. The exampledetection phase 202 includes an example input image 206, an exampleproduct detection CNN model (e.g., first CNN model) 208, example outputdetections 210, an example image cropper 212, and example image crops214. Furthermore, the example classification phase 204 includes anexample query image crop (e.g., processed image crop) 216, an exampleproduct classification CNN model (e.g., second CNN model) 218, andexample query predictions 220 (hereinafter 220A, 220B, 220C, 220D, etc.)with query product descriptions 222 and query confidence levels 224(hereinafter 224A, 224B, 224C, 224D, etc.).

In the illustrated example of FIG. 2 , the detection and classificationmethodology 200 is initiated and/or performed at the detection phase202. In the example detection phase 202, the input image 206 correspondsto shelves displayed in a store with multiple products positioned oneach of the shelves and is provided to the product detection CNN model208. In this example, the input image 206 includes a first shelf and asecond shelf on which multiple beverage products are displayed, and themultiple beverage products vary by size, shape, and/or brand. In someexamples, the input image 206 can be captured by an auditor and/or acustomer of the store using a camera (e.g., on a mobile device).

In response to receiving the input image 206 as an input, the productdetection CNN model 208 detects image portions of the input image 206corresponding to ones of the products captured in the input image 206.In the illustrated example of FIG. 2 , the product detection CNN model208 is a trained model. For example, the product detection CNN model 208is trained based on labeled data to identify image portions that mayrepresent a product. In response to detecting the image portions of theinput image 206, the product detection CNN model 208 outputs the outputdetections 210. For example, the product detection CNN model 208receives the input image 206 and predicts bounding boxes on the inputimage 206 that may contain products. In some examples, the productdetection CNN model 208 outputs coordinates corresponding to each of thebounding boxes relative to the input image 206.

In the illustrated example of FIG. 2 , the output detections 210 areprovided to the image cropper 212. In turn, the image cropper 212 cropseach of the detected image portions identified in the output detections210 from the input image 206 to generate the image crops 214.Accordingly, each of the image crops 214 corresponds to a unique productcaptured in the input image 206. In some examples, the image crops 214correspond to the beverage products on the first shelf and/or the secondshelf of the store. In some examples, the image crops 214 can be used asan input for the classification phase 204 of the detection andclassification methodology 200.

The example classification phase 204 is associated with processing theimage crops 214 generated during the detection phase 202 to generate thequery image crop 216. For example, one of the image crops 214 is resizedand/or reshaped during the classification phase 204 to generate thecorresponding query image crop 216. In some examples, each of the imagecrops 214 is processed prior to execution of the product classificationCNN model 218. In some examples, processing of the image crops 214 canimprove accuracy and/or speed of classification by the productclassification CNN model 218. However, size information corresponding toeach of the products in the image crops 214 can be lost during reshapingof the image crops 214 and/or during capturing of the input image 206.

In the illustrated example of FIG. 2 , the query image crop 216 isprovided (e.g., as an input) to the product classification CNN model218. Further, upon execution, the product classification CNN model 218generates the query predictions 220 corresponding to the query imagecrop 216. For example, the query predictions 220 include the queryproduct descriptions 222 identifying the product captured in the queryimage crop 216. In some examples, the query predictions 220 can identifyone or more possible products that correspond to the query image crop216. In this example, the first query prediction 220A indicates that theproduct in the query image crop 216 is a Coke Zero® with a volume of 0.5liters (L), the second query prediction 220B indicates that the productis a Coke Zero® with a volume of 1 L, the third query prediction 220Cindicates that the product is an original flavor Coke® with a volume of0.5 L, etc. In some examples, a number of the query predictions 220corresponds to a total number of products supported by the productclassification CNN model 218. In some such examples, the total number ofproducts supported by the product classification CNN model 218 can begreater than 50,000.

In the illustrated example of FIG. 2 , the query predictions 220 includethe query confidence levels 224 corresponding to each of the queryproduct descriptions 222. In this example, the query confidence levels224 indicate a likelihood (e.g., a confidence) that the correspondingpredicted query product descriptions 222 are accurate and/or correct. Inthe illustrated example, the first query prediction 220A corresponds toan example first query confidence level 224A of 0.6 (e.g., 60%confidence). As such, the product in the query image crop 216 is 60%likely to be a Coke Zero® with a volume of 0.5 L. In the illustratedexample of FIG. 2 , remaining ones of the query confidence levels 224(e.g., an example second query confidence level 224B, an example thirdquery confidence level 224C, an example fourth query confidence level224D, and an example fifth query confidence level 224E) are lower thanthe first query confidence level 224A. In some examples, a predictioncorresponding to the highest confidence level (e.g., the first queryprediction 220A) is selected to be a correct prediction with respect tothe query image crop 216. In some examples, the product classificationCNN model 218 can output a predetermined number of the query predictions220 (e.g., a top five of the query predictions 220 corresponding to thehighest query confidence levels 224).

In the illustrated example of FIG. 2 , the query predictions 220generated by the product classification CNN model 218 are basedprimarily on the input provided by the query image crop 216. Forexample, the product classification CNN model 218 does not applycontextual information from remaining ones of the image crops 214 duringgeneration of the query predictions 220. The contextual information caninclude a probability that a product in the query image crop 216 has afirst volume in response to a neighboring product (e.g., located on thesame shelf as the query product) having a second volume. In someexamples, the probability can be obtained from a probabilitydistribution represented by a volume co-occurrence graph. According toexamples disclosed herein, the contextual information (e.g., the volumeco-occurrence graph) can be used to update and/or recalibrate the queryconfidence levels 224 generated by the product classification CNN model218.

FIG. 3 illustrates an example confidence generation and recalibrationprocess flow 300 in accordance with teachings of this disclosure. In theillustrated example of FIG. 3 , the confidence generation andrecalibration process flow 300 is used to generate and/or recalibratethe query confidence levels 224 of FIG. 2 . In the illustrated exampleof FIG. 3 , the example confidence generation and recalibration processflow 300 includes examples inputs 302 which, in turn, include theexample query image crop 216 of FIG. 2 and example neighbor image crops304. The example confidence generation and recalibration process flow300 also includes an example classification 306, example outputs 308including the aforementioned example query confidence levels 224 andexample neighbor confidence levels 310, an example confidencerecalibration 312, an example neighbor selection 314, and example volumeco-occurrence distribution graph 316, and example recalibrated queryconfidence levels 318.

In the illustrated example of FIG. 3 , the query image crop 216 and theneighbor image crops 304 are provided as inputs to the classification306. In this example, at the classification 306, the productclassification CNN model 218 of FIG. 2 is executed based on the inputs302 (e.g., the query image crop 216 and the neighbor image crops 304) togenerate the outputs 308 (e.g., the query confidence levels 224 and theneighbor confidence levels 310). In this example, the neighbor imagecrops 304 correspond to neighbor products located on the same shelf as aquery product in the query image crop 216. For example, the neighborimage crops 304 are identified in the output detections 210 of FIG. 2along with the query image crop 216. In some examples, the neighborimage crops 304 are selected based on the locations of the neighborimage crops 304 with respect to the query image crop 216 in the inputimage 206 of FIG. 2 . For example, the neighbor image crops 304 areselected in response to each of the neighbor image crops 304 beingsubstantially at the same vertical position of the input image 206 asthe query image crop 216. In some examples, the neighbor image crops 304correspond to n neighbors, where n is a number of the neighbor productson the same shelf as the query product corresponding to the query imagecrop 216. In some examples, the neighbor image crops 304 include anexample first neighbor image crop 304A, an example second neighbor imagecrop 304B, and up to an example nth neighbor image crop 304C.

In the illustrated example of FIG. 3 , the outputs 308 are output by theclassification 306 in response to execution of the productclassification CNN model 218. In the illustrated example, the queryconfidence levels 224 correspond to the query image crop 216, and theneighbor confidence levels 310 correspond to the neighbor image crops304. In some examples, the neighbor confidence levels 310 correspond topossible product descriptions of the neighbor products. In someexamples, the possible product descriptions for the neighbor image crops304 are the same as the query product descriptions 222 of FIG. 2 .Furthermore, each of the possible product descriptions can include aproduct volume corresponding to the respective neighbor product. In someexamples, the neighbor confidence levels 310 represent confidences thatthe corresponding product descriptions for each of the neighbor imagecrops 304 are correct and/or accurate.

In the illustrated example of FIG. 3 , the confidence recalibration 312utilizes the outputs 308 of the classification 306 to recalibrate thequery confidence levels 224. For example, at the confidencerecalibration 312, the query confidence levels 224 are recalibratedbased on the neighbor confidence levels 310 and the volume co-occurrencedistribution graph 316. In some examples, at the neighbor selection 314,the neighbor confidence levels 310 corresponding to each of the neighborproducts are selected and used to iteratively and/or reiterativelyupdate the query confidence levels 224 to generate the correspondingrecalibrated query confidence levels 318. Additionally, using the volumeco-occurrence distribution graph 316, the query confidence levels 224are updated based on probabilities that first volumes corresponding tothe query product and second volumes corresponding to each of theneighbor products appear on the same shelf. Advantageously, therecalibrated query confidence levels 318 generated at the confidencerecalibration 312 are more accurate compared to the query confidencelevels 224. Recalibration of the query confidence levels 224 is furtherdescribed below in connection with FIG. 4 .

FIG. 4 is a block diagram of an example classification controller 400 inaccordance with teachings of this disclosure. The example classificationcontroller 400 is implemented to generate and/or recalibrate the queryconfidence levels 224 that are generated by the classification phase 204of FIG. 2 . In some other examples, the example classificationcontroller 400 is implemented during the classification phase 204. Inthe illustrated example of FIG. 4 , the classification controller 400includes an example input interface 402, an example model trainer 404,an example co-occurrence graph generator 406, an example image cropdetector 408, an example prediction generator 410, an example confidencerecalibrator 412, an example grouping controller 414, an example reportgenerator 416, and an example database 418 which, in turn, includesand/or stores example labeled data 420 and example image data 422.

The example input interface 402 receives commands and/or data from auser. In some examples, the input interface 402 is communicativelycoupled to a computing device (e.g., a personal computing device, aremote computer system) and/or a network (e.g., via a networkinterface). For example, the input interface 402 can receive the labeleddata 420 and/or the image data 422 from the computing device and/or froma mobile device operated by the user. In some examples, the inputinterface 402 can receive one or more commands from the user via thecomputing device. In some examples, in response to the image data 422including an image (e.g., the input image 206 of FIG. 2 ), the one ormore commands can include directing the classification controller 400 todetect image crops (e.g., the image crops 214 of FIG. 2 ) from the inputimage 206. Additionally or alternatively, in response to the image data422 including the image crops 214, the one or more commands can includedirecting the classification controller 400 to execute the confidencegeneration and recalibration process flow 300 of FIG. 3 based on theimage crops 214. In some examples, the input interface 402 can transmitand/or provide the labeled data 420 and/or the image data 422 to bestored in the database 418.

The model trainer 404 generates and/or trains one or more CNN models(e.g., the product detection CNN model 208 and/or the productclassification CNN model 218 of FIG. 2 ). For example, the model trainer404 can train the one or more CNN models using the labeled data 420stored in the database 418. In examples disclosed herein, the labeleddata 420 refers to data that has been manually reviewed by a humanreviewer. For example, the labeled data 420 can include labeled imagecrops, where labels identifying the products in the labeled image cropsare assigned by the human reviewer. During training, model parameters ofthe one or more CNN models are iteratively updated based on the labeleddata 420 until an error value associated with the one or more CNN modelssatisfies a threshold. In particular, the error value can be calculatedbased on a cross entropy loss function.

In some examples, a first portion of the labeled data 420 is utilizedfor training the one or more CNN models, and a second portion of thelabeled data 420 is used for validating the one or more CNN models. Forexample, the model trainer 404 can train the one or more CNN modelsusing the first portion of the labeled data 420, and can execute the oneor more trained CNN models using the second portion of the labeled data420 to output predicted labels. In such examples, the model trainer 404compares the predicted labels to known labels associated with the secondportion of the labeled data 420. In some examples, the model trainer 404determines a proportion (e.g., percentage) of the predicted labels thatare correct and/or accurate (e.g., a proportion of the predicted labelsmatching corresponding ones of the known labels). In response to theproportion of the predicted labels satisfying a validation threshold(e.g., 90% correct, 95% correct), the model trainer 404 validates and/orselects the one or more trained CNN models for use by the classificationcontroller 400. Alternatively, in some examples, the model trainer 404can retrain the one or more CNN models in response to the proportion ofthe predicted labels not satisfying the validation threshold. In someexamples, in response to the model trainer 404 training and/orvalidating the product detection CNN model 208 and the productclassification CNN model 218, the model trainer 404 deploys the productdetection CNN model 208 for use by the image crop detector 408, anddeploys the product classification CNN model 218 for use by theprediction generator 410.

The co-occurrence graph generator 406 of the illustrated examplegenerates one or more volume co-occurrence graphs based on the labeleddata 420. For example, the co-occurrence graph generator 406 generates avolume co-occurrence cardinality graph based on the number ofco-occurrences of volumes per shelf. In some examples, the co-occurrencegraph generator 406 generates the volume co-occurrence distributiongraph 316 of FIG. 3 by normalizing the volume co-occurrence cardinalitygraph.

FIGS. 5A and 5B illustrate an example volume co-occurrence cardinalitygraph (e.g., cardinality graph) 502 and the example volume co-occurrencedistribution graph (e.g., distribution graph) 316 of FIG. 3 ,respectively, that can be implemented in examples disclosed herein. Inthe illustrated example of FIG. 5A, the cardinality graph 502 is asquare matrix of size N-by-N, where N corresponds to a number of uniquevolumes represented in the labeled data 420. For example, thecardinality graph 502 of FIG. 5A corresponds to N=3, where each of theproducts captured by the labeled data 420 corresponds to one of threeunique volumes (e.g., 0.5 L, 1.0 L, or 1.2 L). The cardinality graph 502includes example rows 506 including an example first row 506Acorresponding to 0.5 L, an example second row 506B corresponding to 1.0L, and an example third row 506C corresponding to 1.2 L. Furthermore,the cardinality graph 502 includes example columns 508 including anexample first column 508A corresponding to 0.5 L, an example secondcolumn 508B corresponding to 1.0 L, and an example third column 508Ccorresponding to 1.2 L.

In the illustrated example of FIG. 5A, each value (e.g., V_(ij)) in thecardinality graph 502 represents a number of instances that a firstvolume (e.g., corresponding to an i^(th) column) and a second volume(e.g., corresponding to a j^(th) row) occur together in the same shelfbased on the labeled data 420. For example, according to the firstcolumn 508A and the second row 506B of the cardinality graph 502, thefirst volume of 0.5 L and the second volume of 1.0 L co-occur 2 times.In particular, in the labeled images of the labeled data 420, there are2 instances in which a first product having the first volume of 0.5 Land a second product having the second volume of 1.0 L appear on thesame shelf. Similarly, the cardinality graph 502 displays the number ofco-occurrences for each respective pair of volumes.

In the illustrated example of FIG. 5B, the distribution graph 316corresponding to the cardinality graph 502 of FIG. 5A can be generatedby normalizing the values in the cardinality graph 502. For example,normalized values in an example first column 510A of the distributiongraph 316 are generated by determining a sum of the values from thefirst column 508A of the cardinality graph 502, then dividing each ofthe values from the first column 508A of the cardinality graph 502 bythe sum. The normalized values for an example second column 510B and anexample third column 510C of the distribution graph 316 can similarly bedetermined based on the second column 508B and the third column 508C ofthe cardinality graph 502, respectively. The normalized values in thedistribution graph 316 represent a probability of the first volumeoccurring in a shelf given the second volume occurring in the shelf. Forexample, according to the first column 510A and an example second row512B of the distribution graph 316, a probability of co-occurrence ofthe first volume of 0.5 L occurring in the shelf given that the secondvolume of 1.0 L occurs in the shelf is approximately 0.17 (e.g., 17%).In particular, in the labeled images of the labeled data 420, a firstproduct having the first volume of 0.5 L appears in 17% of the labeledimages containing a second product having the second volume of 1.0 L.Similarly, the distribution graph 316 displays the probability ofco-occurrence for each respective pair of volumes.

Returning to FIG. 4 , the co-occurrence graph generator 406 can generatethe cardinality graph 502 and the distribution graph 316 of FIGS. 5A and5B, respectively. For example, the co-occurrence graph generator 406generates the cardinality graph 502 by counting the number ofco-occurrence for each volume pair in the labeled data 420. Further, theco-occurrence graph generator 406 generates the distribution graph 316by normalizing the values in the cardinality graph 502. Additionally oralternatively, the co-occurrence graph generator 406 can generate aco-occurrence graph based on at least one different characteristic ofthe products instead of the volumes. For example, the co-occurrencegraph can be based on co-occurrences of color, shape, weight, brand,etc. for products on the same shelf or in the same area. In someexamples, the co-occurrence graph generator 406 provides the cardinalitygraph 502 and/or the distribution graph 316 to the confidencerecalibrator 412. In some examples, the co-occurrence graph generator406 updates the cardinality graph 502 and/or the distribution graph 316in response to the input interface 402 receiving new, updated and/oradditional data to include in the labeled data 420.

The example image crop detector 408 can detect and/or generate the imagecrops 214 from the input image 206 of FIG. 2 . For example, the imagecrop detector 408 can execute the product detection CNN model 208 usingthe input image 206 to output the output detections 210 of FIG. 2 . Insome examples, the output detections 210 correspond to products (e.g.,beverage products) detected in the input image 206. In some examples,the image crop detector 408 can include the image cropper 212 of FIG. 2. In such examples, the image cropper 212 can generate the image crops214 by cropping the output detections 210 from the input image 206 andgenerate separate image files corresponding to the image crops 214.Additionally or alternatively, the image crop detector 408 can processthe image crops 214. For example, in response to generating the imagecrops 214, the image crop detector 408 can reshape and/or resize theimage crops 214. In some such examples, the image crops 214 are resizedto the same pixel dimensions to allow execution of the productclassification CNN model 218. In some examples, the image crop detector408 provides the image crops 214 to the prediction generator 410 and/orto the database 418 to be stored as the image data 422.

The prediction generator 410 generates predictions (e.g., the querypredictions 220 of FIG. 2 ) corresponding to each of the image crops214. For example, in response to the image crop detector 408 processingone of the image crops 214 to generate the query image crop 216, theprediction generator 410 executes the product classification CNN model218 using the query image crop 216. In response to executing the productclassification CNN model 218, the prediction generator 410 generates thequery product descriptions 222 and the corresponding query confidencelevels 224 for the query image crop 216. In this example, the predictiongenerator 410 reorders the query predictions 220 from highest confidenceto lower and/or lowest confidence based on the query confidence levels224, and selects a prediction having a relatively high confidence level(e.g., the maximum confidence level) as an accurate classification forthe product in the query image crop 216.

In the illustrated example of FIG. 4 , the prediction generator 410further generates neighbor predictions corresponding to each of theneighbor image crops 304 of FIG. 3 . For example, the predictiongenerator 410 generates the neighbor predictions by executing theproduct classification CNN model 218 using each of the neighbor imagecrops 304. In such examples, each of the neighbor predictions furtherinclude neighbor product descriptions and the neighbor confidence levels310 of FIG. 3 . In some examples, the prediction generator 410 providesthe neighbor predictions and the query predictions 220 to the confidencerecalibrator 412 for recalibration and/or to the database 418 forstorage.

The confidence recalibrator 412 recalibrates the query confidence levels224 generated by the prediction generator 410 to generate therecalibrated query confidence levels 318 shown in FIG. 3 . For example,the confidence recalibrator 412 recalibrates the query confidence levels224 based on the volume co-occurrence distribution graph 316 and theneighbor predictions corresponding to each of the neighbor image crops304. In the illustrated example of FIG. 4 , the confidence recalibrator412 determines each of the recalibrated query confidence levels 318based on Equation 1 below.

$\begin{matrix}{{\hat{S}}_{i}^{m} = {\frac{1}{N}\frac{1}{K}{\sum_{n \in N}{\sum_{j = 1}^{K}{\log{p\left( {v_{i}^{m}❘v_{j}^{n}} \right)}{p\left( c_{j}^{n} \right)}{p\left( c_{i}^{m} \right)}}}}}} & {{Equation}1}\end{matrix}$

In the above Equation 1, Ŝ_(i) ^(m) represents one of the recalibratedquery confidence levels 318 corresponding to query crop m, where thequery crop m represents the query image crop 216. In some examples, thequery crop m can correspond to a different one of the image crops 214.Furthermore, Ŝ_(i) ^(m) refers to an i^(th) position (e.g., rank) of therecalibrated query confidence levels 318. For example, in response tothe rank i being equal to 1, Ŝ_(i) ^(m) is a recalibrated value for thefirst query confidence level 224A of FIG. 2 . Additionally, in Equation1 above, n refers to a current neighbor crop of the neighbor image crops304, and N represents the total number of the neighbor image crops 304.K represents the total number of product types (e.g., total number ofthe query product descriptions 222 of FIG. 2 ) supported by the productclassification CNN model 218.

In the above Equation 1, p(c_(i) ^(m)) represents a confidencecorresponding to the position for the query crop m. For example, inresponse to the rank i being equal to 1, p(c_(i) ^(m)) corresponds tothe first query confidence level 224A (e.g., 0.6). Furthermore, p(c_(j)^(n)) represents a confidence corresponding to the j^(th) position ofthe neighbor confidence levels 310 for the current neighbor crop n. InEquation 1, p(v_(i) ^(m)|v_(j) ^(n)) represents a conditionalprobability of the first volume v_(i) ^(m) given the second volume v_(j)^(n). In some examples, the confidence recalibrator 412 determines theconditional probability based on the distribution graph 316. In oneexample, in response to the first volume v_(i) ^(m) being 0.5 L and thesecond volume v_(j) ^(n) being 0.5 L, the confidence recalibrator 412determines that the conditional probability is 0.83 based on thedistribution graph 316.

The example confidence recalibrator 412 generates the recalibrated queryconfidence levels 318 based on Equation 1. For example, the confidencerecalibrator 412 determines a logarithm of a product of the conditionalprobability p(v_(i) ^(m)|v_(j) ^(n)), the confidence p(c_(i) ^(m))corresponding to the i^(th) position for the query crop m, and theconfidence p(c_(i) ^(m)) for each j^(th) position of the currentneighbor crop n. Furthermore, the confidence recalibrator 412 determinesa first sum based on the logarithms of the products across all j^(th)positions for the current neighbor crop n. In this example, theconfidence recalibrator 412 further determines a second sum based on thefirst sums for each of the neighbor image crops 304. The confidencerecalibrator 412 can generate the recalibrated value Ŝ_(i) ^(m) bydividing the second sum by the total number of neighbors N and the totalnumber of product types supported K. In some examples, the confidencerecalibrator 412 can generate the recalibrate query confidence levels224 for one or more different query crops (e.g., m) and/or one or moredifferent ranks (e.g., i).

The example grouping controller 414 groups the image crops 214 based onshelf location and/or volume. For example, the grouping controller 414can select the neighbor image crops 304 based on locations of theneighbor image crops 304 with respect to the query image crop 216 in theinput image 206. For example, the grouping controller 414 selects theneighbor image crops 304 corresponding to ones of the image crops 214having the same vertical position (e.g., the same shelf location) in theinput image 206 as the query image crop 216. In some examples, thegrouping controller 414 labels each of the image crops 214 based on thecorresponding shelf location, where a first label corresponds to a firstshelf, a second label corresponds to a second shelf, etc. In someexamples, the grouping controller 414 stores labeled ones of the imagecrops 214 as image data 422 in the database 418. Additionally oralternatively, the grouping controller 414 can group labeled image cropsfrom the labeled data 420 based on the corresponding shelf locations,where the labels are assigned by a human reviewer instead of thegrouping controller 414. In some examples, additionally oralternatively, the grouping controller 414 can group the labeled imagecrops from the labeled data 420 based on volumes of the correspondingproducts in the labeled image crops.

In one example, the grouping controller 414 determines a first group ofthe image crops 214 corresponding to products on a first shelf of theinput image 206, and determines a second group of the image crops 214corresponding to products on a second shelf of the input image 206.Furthermore, the grouping controller 414 can determine a first subgroupof the first group corresponding to products on the first shelf with avolume of 0.5 L, a second subgroup of the first group corresponding toproducts on the first shelf with a volume of 1.0 L, and a third subgroupof the first group corresponding to products on the first shelf with avolume of 1.2 L. In some examples, the grouping controller 414 canprovide each of the groups and the subgroups of the image crops 214 tothe co-occurrence graph generator 406 for generating the cardinalitygraph 502 and/or the distribution graph 316.

The report generator 416 of the illustrated example generates and/orsends one or more reports to a user. In particular, the report generator416 can generate a report including the recalibrated query confidencelevels 318 generated by the confidence recalibrator 412. In someexamples, the report can further include the query product descriptions222, the query confidence levels 224, and/or otherwise the querypredictions 220 of FIG. 2 . In some examples, the report generator 416can send the report to a computing device communicatively coupled to theclassification controller 400 and/or operated by the user. In someexamples, the report generator 416 can be configured to send the reportperiodically and/or in response to a command from the user. Additionallyor alternatively, the report generator 416 can store the report in thedatabase 418.

In this example, the database 418 stores data (e.g., the labeled data420 and the image data 422) utilized and/or generated by theclassification controller 400. The example database 418 of FIG. 4 isimplemented by any memory, storage device and/or storage disc forstoring data such as, for example, flash memory, magnetic media, opticalmedia, solid state memory, hard drive(s), thumb drive(s), etc.Furthermore, the data stored in the example database 418 may be in anydata format such as, for example, binary data, comma delimited data, tabdelimited data, structured query language (SQL) structures, etc. While,in the illustrated example, the example database 418 is illustrated as asingle device, the example database 418 and/or any other data storagedevices described herein may be implemented by any number and/or type(s)of memories.

Turning to FIG. 6 , a block diagram of an example process flow 600 thatcan be implemented in examples disclosed herein is shown. The exampleprocess flow 600 can be executed by the classification controller 400 ofFIG. 4 to detect and/or classify an image (e.g., the input image 206 ofFIG. 2 ). In the illustrated example of FIG. 6 , example data 602 isprovided to example crop detections 604. For example, the data 602 canbe provided via the input interface 402 of FIG. 4 . In some examples,the data 602 can include the input image 206. At the crop detections604, the image crop detector 408 of FIG. 4 detects and crops the imagecrops 214 from the input image 206 by executing the product detectionCNN model 208 of FIG. 2 . In some examples, the product detection CNNmodel 208 is generated, trained, and/or validated by the model trainer404 of FIG. 4 prior to execution of the product detection CNN model 208by the image crop detector 408. In response to the image crops 214 beingdetected, for example group crops by shelf 606, the grouping controller414 of FIG. 4 groups the image crops 214 based on shelf location ofproducts corresponding to the image crops 214. For example, the groupingcontroller 414 groups the query image crop 216 and the neighbor imagecrops 304 in response to determining that the products corresponding tothe query image crop 216 and the neighbor image crops 304 are located onthe same shelf. As such, example crops by shelf 610 includes a group ofthe image crops 214 selected by the grouping controller 414, where thegroup of the image crops 214 corresponds to the inputs 302 of FIG. 3 .

The inputs 302 generated at the crops by shelf 610 are provided to anexample classification 608. At the example classification block 608, theprediction generator 410 generates the query predictions 220 of FIG. 2and neighbor predictions corresponding to each of the neighbor imagecrops 304. For example, at the classification 608, the predictiongenerator 410 provides the query image crop 216 and the neighbor imagecrops 304 as input to the product classification CNN model 218 of FIG. 2to generate the query confidence levels 224 and the neighbor confidencelevels 310 of FIG. 3 , as well as the corresponding query productdescriptions 222 and neighbor product descriptions. In some examples,the product classification CNN model 218 is generated, trained, and/orvalidated by the model trainer 404 prior to execution of the productclassification CNN model 218. In the illustrated example of FIG. 6 , theclassification 608 outputs example shelf crop predictions 612, where theexample shelf crop predictions 612 include the query predictions 220 andthe neighbor predictions (e.g., the query confidence levels 224, theneighbor confidence levels 310, the query product descriptions 222, andthe neighbor product descriptions) corresponding to the query image crop216 and the neighbor image crops 304.

In the illustrated example of FIG. 6 , at an example confidencerecalibration 614, the confidence recalibrator 412 of FIG. 4recalibrates one or more shelf crop confidence levels (e.g., the queryconfidence levels 224 and/or the neighbor confidence levels 310) fromthe shelf crop predictions 612. For example, at the confidencerecalibration 614, the confidence recalibrator 412 recalibrates thequery confidence levels 224 based on the shelf crop predictions 612 andthe volume co-occurrence distribution graph 316 of FIGS. 3 and/or 4B. Insuch examples, the confidence recalibrator 412 recalibrates the queryconfidence levels 224 using Equations 1 and 2 described in connectionwith FIG. 4 above. In the illustrated example of FIG. 6 , examplerecalibrated shelf crop confidence levels 616 (e.g., including therecalibrated query confidence levels 318 of FIG. 3 ) are an output ofthe confidence recalibration 614. In some examples, the process flow 600can be executed by the classification controller 400 in response to acommand from the user. In some examples, the classification controller400 executes the process flow 600 in response to the input interface 402receiving a new input image (e.g., the input image 206) for whichproduct detection and/or product classification can be performed.

FIG. 7 is a block diagram of an example graph generation process flow700 that can be implemented in examples disclosed herein. For example,the classification controller 400 of FIG. 4 can execute the graphgeneration process flow 700 to generate the cardinality graph 502 ofFIG. 5A and/or the distribution graph 316 of FIGS. 3 and/or 5B. In theillustrated example of FIG. 7 , the graph generation process flow 700includes example volume co-occurrence cardinality graph generation(e.g., cardinality graph generation) 702 including an example shelfsplit 704 and an example update graph 706, and further includes examplevolume co-occurrence distribution graph generation (e.g., distributiongraph generation) 708 including an example split by volume 710 andexample normalization 712.

In the illustrated example of FIG. 7 , the labeled data 420 of FIG. 4 isprovided to (e.g., as an input to) the cardinality graph generation 702.For example, the labeled data 420 includes labeled images for whichlabels have been assigned by a human reviewer. For example, the labelscan indicate product descriptions and volumes corresponding to productsin the labeled images. Additionally, in some examples, the labels canindicate shelf locations of the corresponding products. At the shelfsplit 704, the grouping controller 414 of FIG. 4 splits the labeled data420 into groups based on the shelf location of each of the labeledimages. For example, at the shelf split 704, the grouping controller 414can select a first group of the labeled images corresponding to theproducts on a first shelf, and a second group of the labeled imagescorresponding to the products on a second shelf. In some examples, oneor more additional groups are generated corresponding to productslocated on one or more additional shelves. Additionally oralternatively, the grouping controller 414 selects the groups based onthe shelf locations indicated by the labels of the labeled data 420.

In the illustrated example of FIG. 7 , at the update graph 706, thefirst group and/or the second group from the labeled data 420 can beused to generate the cardinality graph 502. For example, at the updategraph 706, the co-occurrence graph generator 406 of FIG. 4 determinesthe number of co-occurrences for each volume pair in the first shelfand/or in the second shelf based on the labeled data 420. In suchexamples, the co-occurrence graph generator 406 determines each value inthe cardinality graph 502 based on the number of co-occurrences betweena first volume corresponding to one of the columns 508 of FIG. 5A and asecond volume corresponding to one of the rows 506 of FIG. 5A. In theillustrated example of FIG. 7 , the cardinality graph 502 is an outputof the update graph 706 and/or, more generally, of the cardinality graphgeneration 702.

In the illustrated example of FIG. 7 , the cardinality graph 502 isprovided to (e.g., provided to as an input) the distribution graphgeneration 708. For example, at the split by volume 710, the groupingcontroller 414 splits the cardinality graph 502 by volume. For example,the grouping controller 414 splits the cardinality graph 502 into thefirst column 508A corresponding to a first volume, the second column508B corresponding to a second volume, and a third column 508Ccorresponding to a third volume. At the normalization 712, theco-occurrence graph generator 406 normalizes the cardinality graph 502to generate the distribution graph 316 of FIGS. 3 and/or 5B. Forexample, the co-occurrence graph generator 406 determines a first sumbased on values in the first column 508A of the cardinality graph 502, asecond sum based on values in the second column 508B of the cardinalitygraph 502, and a third sum based on values in the third column 508C ofthe cardinality graph 502. Furthermore, at the normalization 712, theco-occurrence graph generator 406 divides each of the values in thefirst column 508A by the first sum, divides each of the values in thesecond column 508B by the second sum, and divides each of the values inthe third column 508C by the third sum. In response to dividing thevalues of the cardinality graph 502 by the corresponding first sum, thesecond sum, or the third sum, the co-occurrence graph generator 406generates the distribution graph 316. As such, the distribution graph316 is an output of the normalization 712 and/or, more generally, of thedistribution graph generation 708.

In some examples, the classification controller 400 executes the graphgeneration process flow 700 in response to the input interface 402 ofFIG. 4 receiving the labeled data 420. In some examples, theclassification controller 400 can execute the graph generation processflow 700 to update the cardinality graph 502 and/or the distributiongraph 316 in response to new labeled data being received by the inputinterface 402.

FIG. 8 is a block diagram of an example recalibration process flow 800executed by the classification controller 400 of FIG. 4 to recalibratethe query confidence levels 224 of FIGS. 2 and/or 3 that can beimplemented in examples disclosed herein. In some examples, therecalibration process flow 800 is executed at the confidencerecalibration 614 of FIG. 6 . In the illustrated example of FIG. 8 , therecalibration process flow 800 includes the distribution graph 316 ofFIGS. 3, 5B, 6 and/or 7 , an example query confidence (e.g., C_(q)) 802,an example query volume (e.g., V_(q)) 804, an example neighborconfidence (e.g., C_(n)) 806, and an example neighbor volume (e.g.,V_(n)) 808. In the illustrated example of FIG. 8 , the distributiongraph 316, the query confidence 802, the query volume 804, the neighborconfidence 806, and the neighbor volume 808 are inputs to an examplecalculation 810, where an example updated query confidence 812 isgenerated at the calculation 810.

In the illustrated example of FIG. 8 , the query confidence 802 and thequery volume 804 correspond to an i^(th) rank of the query predictions220 of FIG. 2 . For example, in response to the classificationcontroller 400 recalibrating one of the query confidence levels 224corresponding to a top rank (e.g., i=1), the query confidence 802 andthe query volume 804 based on the first query prediction 220A are 0.6and 0.5 L, respectively. In other examples, the query confidence 802 andthe query volume 804 can correspond to a different rank of the querypredictions 220 (e.g., a second rank, a third rank, etc.). For example,the rank corresponds to the one of the query confidence levels 224 beingrecalibrated. Furthermore, in the illustrated example of FIG. 8 , theneighbor confidence 806 and the neighbor volume 808 also correspond to atop (e.g., i=1) prediction for a first one of the neighbor image crops304 (e.g., the first neighbor image crop 304A). In some examples, thequery confidence 802 is recalibrated based on only a top 1 prediction ofthe first neighbor image crop 304A. In other examples, the queryconfidence 802 can be recalibrated based on top n predictions of thefirst neighbor image crop 304A, where n can be between 1 and the totalnumber of products (e.g., K) supported by the product classification CNNmodel 218 of FIG. 2 .

In the illustrated example of FIG. 8 , at the calculation 810, theconfidence recalibrator 412 of FIG. 4 determines the conditionalprobability between the query volume 804 and the neighbor volume 808(e.g., p(V_(q)|V_(n))) based on the distribution graph 316. In oneexample, in response to the query volume 804 and the neighbor volume 808both being 0.5 L, the confidence recalibrator 412 determines that theconditional probability is 0.83. Furthermore, at the calculation 810,the confidence recalibrator 412 determines a product of the conditionalprobability, the query confidence 802, and the neighbor confidence 806.For examples in which the query image crop 216 only has one neighbor(e.g., the first neighbor image crop 304A), the updated query confidence812 corresponds to a logarithm of the product. In other examples,multiple products are determined corresponding to each of the neighborimage crops 304. In some such examples, at the calculation block 810,the confidence recalibrator 412 determines a first product for the firstneighbor image crop 304A, a second product for the second neighbor imagecrop 304B, etc. In such examples, the confidence recalibrator 412determines a sum of the multiple products, where the updated queryconfidence 812 corresponds to the sum.

While an example manner of implementing the classification controller400 of FIG. 4 is illustrated in FIG. 4 , one or more of the elements,processes and/or devices illustrated in FIG. 4 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example input interface 402, the example model trainer 404,the example co-occurrence graph generator 406, the example image cropdetector 408, the example prediction generator 410, the exampleconfidence recalibrator 412, the example grouping controller 414, theexample report generator 416, the example database 418 and/or, moregenerally, the example classification controller 400 of FIG. 4 may beimplemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. Thus, for example, any of theexample input interface 402, the example model trainer 404, the exampleco-occurrence graph generator 406, the example image crop detector 408,the example prediction generator 410, the example confidencerecalibrator 412, the example grouping controller 414, the examplereport generator 416, the example database 418 and/or, more generally,the example classification controller 400 could be implemented by one ormore analog or digital circuit(s), logic circuits, programmableprocessor(s), programmable controller(s), graphics processing unit(s)(GPU(s)), digital signal processor(s) (DSP(s)), application specificintegrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s))and/or field programmable logic device(s) (FPLD(s)). When reading any ofthe apparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example inputinterface 402, the example model trainer 404, the example co-occurrencegraph generator 406, the example image crop detector 408, the exampleprediction generator 410, the example confidence recalibrator 412, theexample grouping controller 414, the example report generator 416,and/or the example database 418 is/are hereby expressly defined toinclude a non-transitory computer readable storage device or storagedisk such as a memory, a digital versatile disk (DVD), a compact disk(CD), a Blu-ray disk, etc. including the software and/or firmware.Further still, the example classification controller of FIG. 4 mayinclude one or more elements, processes and/or devices in addition to,or instead of, those illustrated in FIG. 4 , and/or may include morethan one of any or all of the illustrated elements, processes anddevices. As used herein, the phrase “in communication,” includingvariations thereof, encompasses direct communication and/or indirectcommunication through one or more intermediary components, and does notrequire direct physical (e.g., wired) communication and/or constantcommunication, but rather additionally includes selective communicationat periodic intervals, scheduled intervals, aperiodic intervals, and/orone-time events.

Flowcharts representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the classification controller 400of FIG. 4 are shown in FIGS. 9, 10 , and/or 11. The machine readableinstructions may be one or more executable programs or portion(s) of anexecutable program for execution by a computer processor and/orprocessor circuitry, such as the processor 1212 shown in the exampleprocessor platform 1200 discussed below in connection with FIG. 12 . Theprograms may be embodied in software stored on a non-transitory computerreadable storage medium such as a CD-ROM, a floppy disk, a hard drive, aDVD, a Blu-ray disk, or a memory associated with the processor 1212, butthe entire programs and/or parts thereof could alternatively be executedby a device other than the processor 1212 and/or embodied in firmware ordedicated hardware. Further, although the example programs are describedwith reference to the flowcharts illustrated in FIGS. 9, 10 , and/or 11,many other methods of implementing the example classification controller400 may alternatively be used. For example, the order of execution ofthe blocks may be changed, and/or some of the blocks described may bechanged, eliminated, or combined. Additionally or alternatively, any orall of the blocks may be implemented by one or more hardware circuits(e.g., discrete and/or integrated analog and/or digital circuitry, anFPGA, an ASIC, a comparator, an operational-amplifier (op-amp), a logiccircuit, etc.) structured to perform the corresponding operation withoutexecuting software or firmware. The processor circuitry may bedistributed in different network locations and/or local to one or moredevices (e.g., a multi-core processor in a single machine, multipleprocessors distributed across a server rack, etc.).

The machine readable instructions described herein may be stored in oneor more of a compressed format, an encrypted format, a fragmentedformat, a compiled format, an executable format, a packaged format, etc.Machine readable instructions as described herein may be stored as dataor a data structure (e.g., portions of instructions, code,representations of code, etc.) that may be utilized to create,manufacture, and/or produce machine executable instructions. Forexample, the machine readable instructions may be fragmented and storedon one or more storage devices and/or computing devices (e.g., servers)located at the same or different locations of a network or collection ofnetworks (e.g., in the cloud, in edge devices, etc.). The machinereadable instructions may require one or more of installation,modification, adaptation, updating, combining, supplementing,configuring, decryption, decompression, unpacking, distribution,reassignment, compilation, etc. in order to make them directly readable,interpretable, and/or executable by a computing device and/or othermachine. For example, the machine readable instructions may be stored inmultiple parts, which are individually compressed, encrypted, and storedon separate computing devices, wherein the parts when decrypted,decompressed, and combined form a set of executable instructions thatimplement one or more functions that may together form a program such asthat described herein.

In another example, the machine readable instructions may be stored in astate in which they may be read by processor circuitry, but requireaddition of a library (e.g., a dynamic link library (DLL)), a softwaredevelopment kit (SDK), an application programming interface (API), etc.in order to execute the instructions on a particular computing device orother device. In another example, the machine readable instructions mayneed to be configured (e.g., settings stored, data input, networkaddresses recorded, etc.) before the machine readable instructionsand/or the corresponding program(s) can be executed in whole or in part.Thus, machine readable media, as used herein, may include machinereadable instructions and/or program(s) regardless of the particularformat or state of the machine readable instructions and/or program(s)when stored or otherwise at rest or in transit.

The machine readable instructions described herein can be represented byany past, present, or future instruction language, scripting language,programming language, etc. For example, the machine readableinstructions may be represented using any of the following languages: C,C++, Java, C#, Perl, Python, JavaScript, HyperText Markup Language(HTML), Structured Query Language (SQL), Swift, etc.

As mentioned above, the example processes of FIGS. 9, 10 , and/or 11 maybe implemented using executable instructions (e.g., computer and/ormachine readable instructions) stored on a non-transitory computerand/or machine readable medium such as a hard disk drive, a flashmemory, a read-only memory, a compact disk, a digital versatile disk, acache, a random-access memory and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm non-transitory computer readable medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C. As used herein in the context ofdescribing structures, components, items, objects and/or things, thephrase “at least one of A and B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. Similarly, as used herein in the contextof describing structures, components, items, objects and/or things, thephrase “at least one of A or B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. As used herein in the context ofdescribing the performance or execution of processes, instructions,actions, activities and/or steps, the phrase “at least one of A and B”is intended to refer to implementations including any of (1) at leastone A, (2) at least one B, and (3) at least one A and at least one B.Similarly, as used herein in the context of describing the performanceor execution of processes, instructions, actions, activities and/orsteps, the phrase “at least one of A or B” is intended to refer toimplementations including any of (1) at least one A, (2) at least one B,and (3) at least one A and at least one B.

As used herein, singular references (e.g., “a”, “an”, “first”, “second”,etc.) do not exclude a plurality. The term “a” or “an” entity, as usedherein, refers to one or more of that entity. The terms “a” (or “an”),“one or more”, and “at least one” can be used interchangeably herein.Furthermore, although individually listed, a plurality of means,elements or method actions may be implemented by, e.g., a single unit orprocessor. Additionally, although individual features may be included indifferent examples or claims, these may possibly be combined, and theinclusion in different examples or claims does not imply that acombination of features is not feasible and/or advantageous.

FIG. 9 is a flowchart representative of machine readable instructions900 which may be executed to implement examples disclosed herein. Forexample, the instructions 900 can be executed by the exampleclassification controller 400 of FIG. 4 to generate and recalibrateconfidence levels (e.g., the query confidence levels 224 of FIG. 2 ) forthe query image crop 216 of FIG. 2 . The instructions 900 of FIG. 9begin as the classification controller 400 receives a command from auser (e.g., via the input interface 402 of FIG. 4 ) to generate and/orrecalibrate the query confidence levels 224 for the input image 206 ofFIG. 2 .

At block 902, the example classification controller 400 generates one ormore models and co-occurrence graphs. For example, the model trainer 404of FIG. 4 generates the product detection CNN model 208 and the productclassification CNN model 218 of FIG. 2 , and the co-occurrence graphgenerator 406 of FIG. 4 generates the cardinality graph 502 of FIG. 5Aand the distribution graph 316 of FIGS. 3 and/or 5B. Block 902 isdescribed in greater detail below in connection with FIG. 10 .

At block 904, the example classification controller 400 detects theimage crops 214 of FIG. 2 from the input image 206. For example, theimage crop detector 408 of FIG. 4 detects the output detections 210 byexecuting the product detection CNN model generated at block 902 withthe input image 106, and crops the output detections 210 to generate theimage crops 214. Additionally or alternatively, at block 902, the imagecrop detector 408 can reshape and/or resize the image crops 214.

At block 906, the example classification controller 400 groups the imagecrops 214 by shelf, for example. In this example, the groupingcontroller 414 of FIG. 4 selects a first group corresponding to firstones of the image crops 214 on a first shelf of the input image 206, andselects a second group corresponding to second ones of the image crops214 on a second shelf of the input image 206. For example, the firstgroup can include the query image crop 216 of FIG. 2 and the neighborimage crops 304 of FIG. 3 . While the first and second shelves aredescribed in this example, any other appropriate type of platform,support, spatial demarcation and/or organization/placement structure canbe implemented instead.

At block 908, the example classification controller 400 executes aneural network model to generate predictions for the image crops 214.For example, the prediction generator 410 of FIG. 4 executes the productclassification CNN model 218 using the query image crop 216 and theneighbor image crops 304 to generate the example shelf crop predictions612 of FIG. 6 that correspond to the first shelf of the input image 206.In such examples, the example shelf crop predictions 612 include thequery predictions 220 and neighbor predictions corresponding to thequery image crop 216 and the neighbor image crops 304, respectively. Insome examples, the query predictions 220 and the neighbor predictionsinclude the query confidence levels 224, the neighbor confidence levels310, the query product descriptions 222 of FIG. 2 , and neighbor productdescriptions. Additionally or alternatively, the prediction generator410 can execute the product classification CNN model 218 to generatepredictions corresponding to the image crops 214 in the second groupcorresponding to the second shelf of the input image 206.

At block 910, the example classification controller 400 selects a querycrop from the image crops 214. In such examples, the query crop is oneof the image crops 214 for which confidence levels (e.g., the queryconfidence levels 224) are to be recalibrated. For example, theconfidence recalibrator 412 selects the query image crop 216 from thefirst group of the image crops 214 corresponding to the first shelf ofthe input image 206. In other examples, the prediction generator 410 canselect a different query crop from among the first group or the secondgroup of the image crops 214.

At block 912, the example classification controller 400 recalibratesconfidences for the query crop. For example, the confidence recalibrator412 recalibrates the query confidence levels 224 based on the shelf croppredictions 612 and the distribution graph 316 corresponding to thefirst group of the image crops 214. Block 912 is further described indetail in connection with FIG. 11 below.

At block 914, the example classification controller 400 determineswhether another query crop is to be selected for recalibration. Forexample, in response to the confidence recalibrator 412 determining thatconfidence levels for another query crop from the image crops 214 are tobe recalibrated (e.g., block 914 returns a result of YES), the processreturns to block 910. Alternatively, in response to the confidencerecalibrator 412 determining that the confidence levels for anotherquery crop are not to be recalibrated and/or determining that theconfidence levels for each of the image crops 214 have been recalibrated(e.g., block 914 returns a result of NO), the process proceeds to block916.

At block 916, the example classification controller 400 reportsrecalibrated confidences. For example, the report generator 416 of FIG.4 generates a report including the recalibrated query confidence levels616 of FIG. 6 , which correspond to the query confidence levels 224. Insome examples, the report generator 416 sends the report to a user via acomputer communicatively coupled to the classification controller 400.Additionally or alternatively, the report can include the shelf croppredictions 612 corresponding to the first shelf and/or to the secondshelf of the input image 206. In some examples, the report generator 416stores the report in the database 418 of FIG. 4 . The process ends.

FIG. 10 is a flowchart representative of machine readable instructions1000 which may be executed to implement examples disclosed herein. Forexample, the instructions 1000 can be executed by the exampleclassification controller 400 of FIG. 4 to generate one or more modelsand co-occurrence graphs in accordance with block 902 of FIG. 9 . Forexample, the instructions 1000 of FIG. 10 begin as the exampleclassification controller 400 is to generate the product detection CNNmodel 208 of FIG. 2 , the product classification CNN model 218 of FIG. 2, the distribution graph 316 of FIG. 3 and/or 5B, and/or the cardinalitygraph 502 of FIG. 5A.

At block 1002, the example classification controller 400 obtains labeledimage data. For example, the model trainer 404 and the co-occurrencegraph generator 406 of FIG. 4 obtain the labeled data 420 from thedatabase 418 of FIG. 4 . In some examples, the labeled data 420 includesdata that has been manually reviewed by a human reviewer. For example,the labeled data 420 can include labeled image crops manually reviewedby a human reviewer, where the labeled image crops include labelsidentifying the products and/or shelf location of the products in thelabeled image crops.

At block 1004, the example classification controller 400 trains one ormore neural network models. For example, the model trainer 404 of FIG. 4trains the one or more neural network models using the labeled data 420to generate the product detection CNN model 208 and the productclassification CNN model 218. In some examples, the model trainer 404trains the product detection CNN model 208 and the productclassification CNN model 218 using a first portion of the labeled data420, and validates the product detection CNN model 208 and the productclassification CNN model 218 using a second portion of the labeled data420.

At block 1006, the example classification controller 400 groups thelabeled data 420 based on shelf location, for example. In this example,the grouping controller 414 of FIG. 4 groups the labeled image cropsfrom the labeled data 420 into a first group corresponding to a firstshelf and a second group corresponding to a second shelf. In someexamples, the grouping controller 414 groups the labeled image cropsbased on the corresponding labels of the labeled image crops.

At block 1008, the example classification controller 400 generates avolume co-occurrence cardinality graph (e.g., the cardinality graph502). For example, the co-occurrence graph generator 406 of FIG. 4generates the cardinality graph 502 corresponding to the first group andthe second group of the labeled image crops from the labeled data 420.In such an example, the co-occurrence graph generator 406 determines thenumber of co-occurrence for each volume pair in the labeled data 420 togenerate the cardinality graph 502.

At block 1010, the example classification controller 400 groups thevolume co-occurrence cardinality graph 502 by volume. For example, theco-occurrence graph generator 406 identifies the first column 508A ofthe cardinality graph 502 corresponding to a first volume, the secondcolumn 508B of the cardinality graph 502 corresponding to a secondvolume, and the third column 508C of the cardinality graph 502corresponding to a third volume.

At block 1012, the example classification controller 400 generates avolume co-occurrence distribution graph (e.g., the distribution graph316) by normalizing the cardinality graph 502. For example, theco-occurrence graph generator 406 determines a first sum for values inthe first column 508A, a second sum for values in the second column508B, and a third sum for values in the third column 508C. In such anexample, the co-occurrence graph generator 406 divides each of thevalues in the first column 508A by the first sum, divides each of thevalues in the second column 508B by the second sum, and divides each ofthe values in the third column 508C to normalize the cardinality graph502. As such, the co-occurrence graph generator 406 generates thedistribution graph 316 based on normalized values from the cardinalitygraph 502.

At block 1014, the example classification controller 400 stores the oneor more neural network models and the distribution graph 316. Forexample, the model trainer 404 stores the product detection CNN model208 and the product classification CNN model 218 in the database 418 ofFIG. 4 , and the co-occurrence graph generator 406 stores thecardinality graph 502 and the distribution graph 316 in the database418. Additionally or alternatively, the model trainer 404 provides theproduct detection CNN model 208 to the image crop detector 408 andprovides the product classification CNN model 218 to the predictiongenerator 410. In some examples, the co-occurrence graph generator 406provides the distribution graph to the confidence recalibrator 412 ofFIG. 4 . The process returns to the instructions 900 of FIG. 9 .

FIG. 11 is a flowchart representative of machine readable instructions1100 which may be executed to implement examples disclosed herein. Forexample, the instructions 1100 can be executed by the exampleclassification controller 400 of FIG. 4 to recalibrate confidences inaccordance with block 912 of FIG. 9 . For example, the instructions 1100of FIG. 11 begin as the example classification controller 400 is torecalibrate a top query confidence (e.g., the first query confidencelevel 224A equal to 0.6) from the query confidence levels 224 of FIG. 2corresponding to the query image crop 216 of FIG. 2 . In other examples,the example classification controller 400 executes the instructions 1100to configure a different one of the query confidence levels 224 (e.g.,the second query confidence level 224B, the third query confidence level224C, etc.).

At block 1102, the example classification controller 400 selects acurrent neighbor of the query image crop 216. For example, theconfidence recalibrator 412 of FIG. 4 selects the current neighbor(e.g., corresponding to an index n) from the neighbor image crops 304 ofFIG. 3 . In some examples, the confidence recalibrator 412 of FIG. 4selects a first neighbor (e.g., n=1) from the neighbor image crops 304as the current neighbor.

At block 1104, the example classification controller 400 selects acurrent rank (e.g., j) of the neighbor confidence levels 310 of FIG. 3 .For example, the confidence recalibrator 412 selects a first rank (e.g.,j=1) corresponding to a first position of the neighbor confidence levels310 for the current neighbor.

At block 1106, the example classification controller 400 determines aprobability of co-occurrence. For example, the confidence recalibrator412 determines the probability of co-occurrence (e.g., p(V_(q)|V_(n)))between a query volume and a current neighbor volume based on thedistribution graph 316 of FIGS. 3 and/or 5B. In such an example, thequery volume (e.g., V_(q)) is determined based on the first queryprediction 220A corresponding to the first query confidence level 224A(e.g., 0.5 L), and the current neighbor volume (e.g., V_(n)) is based ona first neighbor prediction corresponding to the selected rank j and thecurrent neighbor n. In some examples, the confidence recalibrator 412determines the probability of co-occurrence p(V_(q)|V_(n)) from a columnof the distribution graph 316. corresponding to the query volume and arow of the distribution graph 316 corresponding to the neighbor volume.

At block 1108, the example classification controller 400 determines aproduct of the probability of co-occurrence, the first query confidencelevel 224A, and the one of the neighbor confidence levels 310corresponding to the current rank. For example, the confidencerecalibrator 412 determines the product by multiplying the probabilityof co-occurrence (e.g., p(V_(q)|V_(n))) by the first query confidencelevel 224A (e.g., C_(a)) and a first neighbor confidence level (e.g.,C_(n)) from the neighbor confidence levels 310 corresponding to thecurrent rank j and the current neighbor n.

At block 1110, the example classification controller 400 determineswhether another rank is to be selected as the current rank j. Forexample, in response to the confidence recalibrator 412 determining thatanother rank is to be selected (e.g., block 1110 returns a result ofYES), the process returns to block 1104 where the confidencerecalibrator 412 selects another rank (e.g., a second rank j=2) todetermine a second product for the current neighbor n. Alternatively, inresponse to the confidence recalibrator 412 determining that anotherrank is not to be selected (e.g., block 1110 returns a result of NO),the process proceeds to block 1112. For example, the confidencerecalibrator 412 can be configured to recalibrate the first queryconfidence level 224A based only on the top rank (e.g., j=1) of theneighbor confidence levels 310.

At block 1112, the example classification controller 400 determines asum of logarithms of the products for the current neighbor n. Forexample, the confidence recalibrator 412 determines a first logarithm ofthe first product corresponding to the first rank (e.g., j=1) and asecond logarithm of the second product corresponding to the second rank(e.g., j=2), then determines a sum of the first logarithm and the secondlogarithm. In other examples, the confidence recalibrator 412 determinesthe sum based on logarithms of products corresponding to each rank jfrom 1 to K, where K is a total number of the neighbor confidence levels310 corresponding to the current neighbor n. In some examples in whichonly the first rank (e.g., j=1) is considered, the confidencerecalibrator 412 determines the logarithm of the product generated atblock 1108.

At block 1114, the example classification controller 400 determineswhether another neighbor is to be selected as the current neighbor n.For example, in response to the confidence recalibrator 412 determiningthat another neighbor is to be selected (e.g., block 1114 returns aresult of YES), the process returns to block 1102 where the confidencerecalibrator 412 selects another neighbor (e.g., a second neighbor n=2)to determine a second sum of logarithms. Alternatively, in response tothe confidence recalibrator 412 determining that another neighbor is notto be selected (e.g., block 1114 returns a result of NO), the processproceeds to block 1116. For example, the confidence recalibrator 412determines that another neighbor is not to be selected in response todetermining a sum of logarithms corresponding to each of the neighborimage crops 304 from n=1 to n=N, where N is the total number of theneighbor image crops 304.

At block 1116, the example classification controller 400 determines atotal sum of the sums of logarithms for the neighbor image crops 304.For example, the confidence recalibrator 412 determines the total sumbased on the sum of logarithms for each of the neighbor image crops 304(e.g., the first neighbor n=1, the second neighbor n=2, etc.) from n=1to n=N.

At block 1118, the example classification controller 400 determines anupdated first query confidence level corresponding to the first queryconfidence level 224A. For example, the confidence recalibrator 412determines that the updated first query confidence level corresponds tothe total sum determined at block 1116 for the first query confidencelevel 224A. In some examples, the report generator 416 of FIG. 4 outputsa report including the recalibrated query confidence levels 318 of FIG.3 , where the recalibrated query confidence levels 318 include theupdated first query confidence level. The process ends. In someexamples, the process of FIG. 11 can be executed for each of the queryconfidence levels 224 corresponding to the query image crop 216. In someexamples, the process of FIG. 11 can be executed for a number (e.g., 10)of the highest confidence levels 224.

FIG. 12 is a block diagram of an example processor platform 1200structured to execute the instructions of FIGS. 9, 10 , and/or 11 toimplement the classification controller 400 of FIG. 4 . The processorplatform 1200 can be, for example, a server, a personal computer, aworkstation, a self-learning machine (e.g., a neural network), a mobiledevice (e.g., a cell phone, a smart phone, a tablet such as an iPad™), apersonal digital assistant (PDA), an Internet appliance, a DVD player, aCD player, a digital video recorder, a Blu-ray player, a gaming console,a personal video recorder, a set top box, a headset or other wearabledevice, or any other type of computing device.

The processor platform 1200 of the illustrated example includes aprocessor 1212. The processor 1212 of the illustrated example ishardware. For example, the processor 1212 can be implemented by one ormore integrated circuits, logic circuits, microprocessors, GPUs, DSPs,or controllers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor implements the example input interface 402,the example model trainer 404, the example co-occurrence graph generator406, the example image crop detector 408, the example predictiongenerator 410, the example confidence recalibrator 412, the examplegrouping controller 414, and the example report generator 416.

The processor 1212 of the illustrated example includes a local memory1213 (e.g., a cache). The processor 1212 of the illustrated example isin communication with a main memory including a volatile memory 1214 anda non-volatile memory 1216 via a bus 1218. The volatile memory 1214 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random AccessMemory (RDRAM®) and/or any other type of random access memory device.The non-volatile memory 1216 may be implemented by flash memory and/orany other desired type of memory device. Access to the main memory 1214,1216 is controlled by a memory controller.

The processor platform 1200 of the illustrated example also includes aninterface circuit 1220. The interface circuit 1220 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 1222 are connectedto the interface circuit 1220. The input device(s) 1222 permit(s) a userto enter data and/or commands into the processor 1212. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 1224 are also connected to the interfacecircuit 1220 of the illustrated example. The output devices 1224 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 1220 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 1220 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 1226. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 1200 of the illustrated example also includes oneor more mass storage devices 1228 for storing software and/or data.Examples of such mass storage devices 1228 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine executable instructions 1232 of FIGS. 9, 10 , and/or 11 maybe stored in the mass storage device 1228, in the volatile memory 1214,in the non-volatile memory 1216, and/or on a removable non-transitorycomputer readable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that classifyproducts from images based on contextual information. The disclosedmethods, apparatus and articles of manufacture improve the efficiency ofusing a computing device by recalibrating confidence levelscorresponding to image classifications generated by the computingdevice, the recalibrated confidence levels to improve accuracy of theimage classifications. The disclosed methods, apparatus and articles ofmanufacture are accordingly directed to one or more improvement(s) inthe functioning of a computer.

Example 1 includes an apparatus to classify an image. The exampleapparatus of Example 1 includes an image crop detector to detect a firstimage crop from the image, the first image crop corresponding to a firstobject, a grouping controller to select a second image cropcorresponding to a second object proximate a location of the firstobject, a prediction generator to, in response to executing a trainedmodel, determine (a) a label corresponding to the first object and (b) aconfidence level associated with the label, and a confidencerecalibrator to recalibrate the confidence level by calculating aprobability of the first object having a first attribute based on thesecond object having a second attribute, the second attribute identifiedbased on the second image crop, the confidence level recalibrated toincrease an accuracy of the image classification.

Example 2 includes the apparatus of Example 1, where the label indicatesa product type of the first object and the first attribute of the firstobject.

Example 3 includes the apparatus of Example 1, where the confidencerecalibrator calculates the probability based on a co-occurrencedistribution graph.

Example 4 includes the apparatus of Example 3, and further includes aco-occurrence graph generator to generate a co-occurrence cardinalitygraph based on labeled data and generate the co-occurrence distributiongraph by normalizing the co-occurrence cardinality graph.

Example 5 includes the apparatus of Example 1, where the trained modelis a trained convolutional neural network model.

Example 6 includes the apparatus of Example 1, where the label is afirst label, the confidence level is a first confidence level, theprediction generator to generate a second label corresponding to thesecond object and a second confidence level associated with the secondlabel, and the confidence recalibrator to recalibrate the firstconfidence level further based on the second confidence level.

Example 7 includes the apparatus of Example 1, where the groupingcontroller selects the second image crop in response to determining thatthe first object and the second object are positioned on a same shelf.

Example 8 includes a method for classifying an image. The example methodof Example 8 includes detecting, by executing instructions with at leastone processor, a first image crop from the image, the first image cropcorresponding to a first object, selecting, by executing instructionswith the at least one processor, a second image crop corresponding to asecond object proximate a location of the first object, determining, byexecuting instructions with the at least one processor, (a) a labelcorresponding to the first object and (b) a confidence level associatedwith the label based on a trained model, and recalibrating, by executinginstructions with the at least one processor, the confidence level bycalculating a probability of the first object having a first attributebased on the second object having a second attribute, the secondattribute identified based on the second image crop, the confidencelevel recalibrated to increase an accuracy of the image classification.

Example 9 includes the method of Example 8, where the label indicates aproduct type of the first object and the first attribute of the firstobject.

Example 10 includes the method of Example 8, and further includesdetermining, by executing instructions with the at least one processor,the probability based on a co-occurrence distribution graph.

Example 11 includes the method of Example 10, and further includesgenerating, by executing instructions with the at least one processor, aco-occurrence cardinality graph based on labeled data and generating, byexecuting instructions with the at least one processor, theco-occurrence distribution graph by normalizing the co-occurrencecardinality graph.

Example 12 includes the method of Example 8, where the trained model isa trained convolutional neural network model.

Example 13 includes the method of Example 8, where the label is a firstlabel, the confidence level is a first confidence level, and furtherincluding generating, by executing instructions with the at least oneprocessor, a second label corresponding to the second object and asecond confidence level associated with the second label, andrecalibrating, by executing instructions with the at least oneprocessor, the first confidence level further based on the secondconfidence level.

Example 14 includes the method of Example 8, where the selecting thesecond image crop includes determining that the first object and thesecond object are positioned on a same shelf.

Example 15 includes a non-transitory computer readable medium includinginstructions which, when executed, cause at least one processor todetect a first image crop from an image, the first image cropcorresponding to a first object, select a second image cropcorresponding to a second object proximate a location of the firstobject, determine, in response to executing a trained model, (a) a labelcorresponding to the first object and (b) a confidence level associatedwith the label, and recalibrate the confidence level based oncalculating a probability of the first object having a first attributebased on the second object having a second attribute, the secondattribute identified based on the second image crop, the confidencelevel recalibrated to increase an accuracy of a classification of theimage.

Example 16 includes the non-transitory computer readable medium ofExample 15, where the label indicates a product type of the first objectand the first attribute of the first object.

Example 17 includes the non-transitory computer readable medium ofExample 15, where the instructions, when executed, further cause the atleast one processor to calculate the probability based on aco-occurrence distribution graph.

Example 18 includes the non-transitory computer readable medium ofExample 17, where the instructions, when executed, further cause the atleast one processor to generate a co-occurrence cardinality graph basedon labeled data and generate the co-occurrence distribution graph bynormalizing the co-occurrence cardinality graph.

Example 19 includes the non-transitory computer readable medium ofExample 15, where the trained model is a trained convolutional neuralnetwork model.

Example 20 includes the non-transitory computer readable medium ofExample 15, where the label is a first label, the confidence level is afirst confidence level, and where the instructions, when executed,further cause the at least one processor to generate a second labelcorresponding to the second object and a second confidence levelassociated with the second label, and recalibrate the first confidencelevel further based on the second confidence level.

Example 21 includes the non-transitory computer readable medium ofExample 20, where the instructions, when executed, further cause the atleast one processor to select the second image crop in response todetermining that the first object and the second object are positionedon a same shelf.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

The following claims are hereby incorporated into this DetailedDescription by this reference, with each claim standing on its own as aseparate embodiment of the present disclosure.

1. (canceled)
 2. An apparatus to classify an image, the apparatuscomprising: memory; machine readable instructions; and processorcircuitry to execute the machine readable instructions to: access afirst image crop from an image having a plurality of objects, the firstimage crop including a first object of the plurality of objects; accessa second image crop from the image, the second image product including asecond object of the plurality of objects; execute a machine learningmodel to determine a label corresponding to the first object and aconfidence level associated with the label; and improve an accuracy ofthe image classification by updating the confidence level based on (a) adetermination the first and second objects are proximate each other inthe image and (b) a likelihood the first object has a firstcharacteristic and the second object has a second characteristic, thesecond characteristic identified based on the second image crop.
 3. Theapparatus of claim 2, wherein the label is a first product descriptionand the first characteristic is a first volume associated with the firstproduct description.
 4. The apparatus of claim 3, wherein the secondcharacteristic is a second volume associated with a second productdescription corresponding to the second object.
 5. The apparatus ofclaim 4, wherein the processor circuitry is to execute the machinelearning model to determine the second product description correspondingto the second object.
 6. The apparatus of claim 4, wherein the processorcircuitry is to determine the likelihood based on a volume co-occurrencedistribution graph.
 7. The apparatus of claim 6, wherein the processorcircuitry is to generate a volume co-occurrence cardinality graph basedon labeled data and generate the volume co-occurrence distribution graphby normalizing the volume co-occurrence cardinality graph.
 8. Theapparatus of claim 2, wherein the image is an image of one or moreshelves having products, and wherein the processor circuitry is toselect the second image crop based on a determination the second objectis on a same shelf as the first object.
 9. The apparatus of claim 2,wherein the first and second characteristics include at least one ofcolor, weight, shape, or brand.
 10. The apparatus of claim 2, whereinthe machine learning model is a Convolutional Neural Network (CNN)model.
 11. The apparatus of claim 2, wherein the processor circuity isto periodically generate a report with the label and the updatedconfidence level and transmit the report to a computing device.
 12. Anon-transitory computer readable medium comprising instructions which,when executed, cause at least one processor to: access a first imagecrop from an image having a plurality of objects, the first image cropincluding a first object of the plurality of objects; access a secondimage crop from the image, the second image product including a secondobject of the plurality of objects; execute a machine learning model todetermine a label corresponding to the first object and a confidencelevel associated with the label; and update the confidence level basedon (a) a determination the first and second objects are proximate eachother in the image and (b) a likelihood the first object has a firstcharacteristic and the second object has a second characteristic, thesecond characteristic identified based on the second image crop, theconfidence level updated to increase an accuracy of the imageclassification.
 13. The non-transitory computer readable medium of claim12, wherein the label is a first product description and the firstcharacteristic is a first volume associated with the first productiondescription.
 14. The non-transitory computer readable medium of claim13, wherein the second characteristic is a second volume associated witha second product description corresponding to the second object.
 15. Thenon-transitory computer readable medium of claim 14, wherein theinstructions cause the at least one processor to execute the machinelearning model to determine the second product description correspondingto the second object.
 16. The non-transitory computer readable medium ofclaim 14, wherein the instructions cause the at least one processor tocalculate the likelihood based on a volume co-occurrence distributiongraph.
 17. The non-transitory computer readable medium of claim 16,wherein the instructions cause the at least one processor to generate avolume co-occurrence cardinality graph based on labeled data andgenerate the volume co-occurrence distribution graph by normalizing thevolume co-occurrence cardinality graph.
 18. The non-transitory computerreadable medium of claim 12, wherein the image is an image of one ormore shelves having products, and wherein the instructions cause the atleast one processor to identify the second image crop based on adetermination the second object is on a same shelf as the first object.19. The non-transitory computer readable medium of claim 12, wherein thefirst and second characteristics include at least one of color, weight,shape, or brand.
 20. The non-transitory computer readable medium ofclaim 12, wherein the machine learning model is a Convolutional NeuralNetwork (CNN) model.
 21. The non-transitory computer readable medium ofclaim 12, wherein the instructions cause the at least one processor toperiodically generate a report with the label and the updated confidencelevel and transmit the report of a computing device.